University of Groningen Patient participation in pharmacovigilance Rolfes, Leàn

Patient participation in pharmacovigilance

Rolfes, Leàn

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Contribution of patient

reports to signal

detection

4.1

Does patient reporting

lead to earlier detection

of drug safety signals?

a retrospective observational

comparative study between

adverse drug reaction reports

by patients and healthcare

professionals

Leàn Rolfes Florence van Hunsel Ola Caster Henric Taavola Katja Taxis Eugène van Puijenbroek

aBsTRaCT

Objective: To explore if there is a difference between patients and healthcare

profes-sionals (HCPs) in time to reporting drug-adverse drug reaction (ADR) associations which led to drug safety signals.

Design: This was a retrospective observational comparative study about ADR reports

by patients and HCPs on time to reporting of selected drug-ADR associations which led to drug safety signals.

Setting: ADR reports were selected from the World Health Organisation Global

data-base of individual case safety reports, VigiBase.

Signals: Reports were selected by using 60 associations described in signals detected

by the Netherlands Pharmacovigilance Centre Lareb between 2011 and 2015.

Main outcome measures: Primary outcome was the difference in time to reporting

between patients and HCPs. The date of the first report for each individual signal was used as time zero. The difference in time between the date of the reports and time zero was calculated. Statistical differences in timing were analysed on the cor-responding survival curves using a Mann-Whitney U test.

Results: In total 2822 reports were included, of which 52.7% were patient reports,

with a median of 25% for all included signals. Overall, HCPs reported earlier than patients: median 7.0 vs 8.3 years (p <0.001).

Conclusions: Patients contributed a large proportion of reports on drug-ADR pairs

that eventually became signals. For all signals, median time to signal detection was 10.4 years. HCPs generally reported 1.3 year earlier than patients. These findings strengthen the evidence on the value of patient reporting in signal detection, and highlight an opportunity to encourage patients to report suspected ADRs even earlier in the future.

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Pharmacovigilance centres around the world have an important role to monitor the safety of drugs in the postmarketing phase. They collect information about adverse drug reactions (ADRs) spontaneously reported by healthcare professionals and pa-tients, for example by the Yellow Card Scheme in the UK. Having patients directly reporting to the national pharmacovigilance centres is relatively new in most areas of the world. In 2012 in the European Union, it became mandatory by law for countries to give patients the opportunity to report possible ADRs directly to the competent authority, although a number of countries introduced reporting by patients earlier [1;2]. In some countries, like the USA, patients have already been able to report for decades. Reports from patients are a well-established source of information in drug safety [3]. Despite patient participation gaining more and more attention worldwide, this does not necessarily mean that countries have fully embraced patient reporting [4;5]. More experience and sharing of information between countries is needed to fully understand its value.

Studies already demonstrated that reports by patients positively contribute to pharmacovigilance. Patients generally give an adequate description of the course of clinical symptoms and they seem more likely to report on the impact of ADRs on their daily life compared to healthcare professionals [6;7]. Some studies found that patients are likely to report more serious ADRs compared to healthcare professionals, while others demonstrate the opposite [8-12]. There are also studies that demon-strated no difference between both groups [6;7;13;14]. Although there have been concerns about the quality of patient reports in the past, it has recently been shown that the clinical quality of information reported by patients is comparable to that of healthcare professionals [15]. Concerning the detection of new drug safety signals, it was demonstrated that reports by patient are taken into account [16-19]. These signals include ADRs not listed in the Summary of Product Characteristics (SPC) and new aspects of known ADRs. A recent study in the Netherlands exploring signals detected from 2010 to 2015 showed that the number of reports directly from patients in the signals rose from 16 (10% of total) in 2010 to 161 (28.3% of total) in 2015 [16]. There were 137 serious reports in all examined signals (30.8% of all patient reports) compared to 224 healthcare professional reports (19.2% of total reports).

Less is known about the difference in timing of reporting by patients and health-care professionals. It has been suggested that reporting by patients contributes to an earlier detection of drug safety signals [20;21]. Indeed, a certain number of reports is necessary to generate new drug safety signals and reports by patients provide an additional source of information. In addition, patients may report earlier on certain ADRs compared to healthcare professionals; for the latter group one of the reasons for

not reporting a possible ADR to a pharmacovigilance centre may be the uncertainty that it actually concerns an ADR.

Little is known about the extent to which patient reports might impact on timely signal detection and whether this is different for ADRs classified as so called ‘impor-tant medical events’ (IMEs), defined as those events that result in death or require (prolonged) hospitalization, and those not classified as IMEs [22;23]. Furthermore, comparing the USA and Europe may provide additional insights given the extensive experience with patient reporting in the USA, versus Europe where patient reporting is relatively new. In the USA there has been a relatively constant flow of patient reports over time, while in most European countries the number of patient reports continues to rise [3;24;25]. Also, in the USA patient reports are mostly received through pharmaceutical companies, while in Europe patients mostly report directly to the national pharmacovigilance centre [2].

This study aims to explore if there is a difference between patients and healthcare professionals in time to reporting drug-ADR associations which led to drug safety signals. The secondary aims are to explore if there is a difference in time to reporting between patients and healthcare professionals for drug safety signals characterized as IMEs, and if there is a difference for reports from those regions with a long history of patient reporting (USA) versus a region with a short history of patient reporting (Europe).

meThoD

Study design and data source

This was a retrospective observational comparative study about ADR reports by patients and healthcare professionals on time to reporting of selected drug-ADR as-sociations that were subsequently classified as drug safety signals. ADR reports were selected from the WHO global database of individual case safety reports, VigiBase. As of June 2017, this database contained over 15 million ADR reports received from over 120 member countries of the WHO programme for international drug monitoring [26].

We selected all reports of drug-ADR associations present in all drug safety signals detected by the Netherlands Pharmacovigilance Centre Lareb between 2011 and 2015. At Lareb, reports by patients were handled in the same way as those from healthcare professionals and they were fully integrated into the process of signal detection. During signal detection, qualitative aspects as well as quantitative aspects (disproportionality analysis) are taken into account [27;28]. Signals covered a wide

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range of different ADRs. We excluded signals on drug interactions, multiple suspected drugs, and dosing or administration errors. All signals are publicly accessible on the Lareb website [29;30]. In total, 60 signals were included in this study.

Based on the drug-ADR associations present in the selected signals, ADR reports were selected from a frozen VigiBase version as of October 2015. Selection of reports in VigiBase was based on the WHO drug classification system, the ATC-5 code or the drug’s brand name [31] and the Medical Dictionary for Regulatory Activities MedDRA Preferred Term coding [32], depending on the drug-ADR association described in the signal. The drug needed to be classified as ‘suspected’ or ‘interacting’ on the reports. Reports had to be filed in the database before dissemination of the drug safety signals.

Only reports that had the E2B structure, an international standard for transmitting ADR reports, were included. Only reports that were either pure patient reports (E2B reports with a single reporter whose qualification was ‘Consumer or other non-health professional’) or pure healthcare professional reports (E2B reports with a single reporter whose qualification was ‘Physician’, ‘Pharmacist’, or ‘Other health profes-sional’) were included. There was no exclusion of duplicate reports; in case the event had been reported by different sources, these were all taken into account.

We only included data from countries if they accepted reports from patients at the time of the first report for the specific drug-ADR association in VigiBase. Start date of patient reporting in the specific countries was obtained from literature [2] or through personal contacts with the national pharmacovigilance centres. This was to ensure that countries not only formally accepted patient reports but actually did so in practice. We excluded data from countries with no patient reports in VigiBase. See Figure 1 for a flowchart of the Methods of data collection.

Outcomes

The primary outcome was the difference in time to reporting between patients and healthcare professionals. The secondary outcomes were the differences in time to reporting between patients or healthcare professionals for (i) IMEs versus non-IMEs, according to the European Medicines Agency (EMA)-list of Important Medical Events, according to MedDRA terminology [18], and (ii) for the USA versus Europe. For Europe, we included countries within the European Union, as well as Iceland and Norway because they participate in EMA regulatory decision making. Although Switzerland, does not participate in EMA regulatory decision making, this country accepts reports directly from patients since 2002 and shares a similar culture with the rest of Europe. For this reason, we decided to take Switzerland into account as well.

Analysis

The date of the first report for each individual signal was used as time zero. All reports on the same drug-ADR association from time zero until signal detection were

Selection of all signals detected by

Lareb between 2011-2015: total of 104

For the 60 Dutch signals, ADR reports with the

same association selected from VigiBase

3824 ADR reports selected, coming from

37 countries

Check date of the first report in VigiBase for all

individual 60 signals Check start date of

patient reporting for each country

For each individual signal: Does the country accept patient reports at

time of first report in VigiBase?

2822 ADR reports

included 1002 ADR reports excluded

YES NO

Reports flagged:  IME/nonIME  USA/Europe/Other

After exclusion criteria: 60 signals included

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included. We calculated the difference in time between time zero and the following reports from patients and healthcare professionals for each signal individually. Subse-quently, data for all signals were pooled. The percentage of reports originating from patients was calculated and it was determined whether a healthcare professional or a patient made the first report for each signal.

Kaplan Meier plots were used to visualize the reporting over time by patients and healthcare professionals, respectively. Statistical differences in time to reporting between patients and healthcare professionals were explored on the corresponding survival curves using Mann-Whitney U tests. To investigate the secondary outcomes, sub-analyses were made for signals classified as (non)IMEs and reports from the USA and Europe. In addition, time to reporting was analysed for healthcare professionals in the USA versus Europe, and patients in the USA versus Europe. Statistical signifi-cance was based on a p-value less than 0.05. Data were analysed using the statistical software program SPSS Statistics, version 22.0 (SPSS, Chicago, IL).

There may be a large difference between reporting of the first report and the time to signal detection for the individual signals. To explore the meaning of the obtained difference in time to reporting between patients and healthcare professionals, relative differences defined as the difference in median time to reporting by patients and healthcare professionals divided by the total time until signal detection, were ana-lysed. The difference in median between both groups was plotted against the total number of days until signal detection. For calculating the median, all signals with at least three patients and three healthcare professional reports were included.

ResuLTs

Characteristics of included signals

In total 60 signals were included (Table 1). The median time to signal detection, calculated from the date of the first report for each individual signal, was 10.4 years, with an inter quartile range of 7.6 – 13.6 years. The signals included a total number of 2822 reports, of which 1488 (52.7%) were reported by patients and 1334 (47.3%) by healthcare professionals. The proportion of patient reports in the individual signals ranged from 0% to 84.4%, with a median of 25.0%. A total of 13 signals (21.7%) did not contain any reports from patients. For 12 signals (20.0%) the first report was made by a patient, for 48 (80.0%) by a healthcare professional.

A total of 18 (30.0%) signals were classified as IME (Table 1, signals in italic) [18]. Overall, IMEs included fewer reports from patients compared to healthcare profes-sionals, range 0% – 55.1% (median of 7.2%) versus non-IMEs 0% – 84.4% (median

Table 1. Description of the 60 drug safety signals

Drug aDR Total

number of reports number of healthcare professional reports number of patient reports mann-whitney u test, p-value Ratio†

Olanzapine Cerebrovascular accident 185 83 102 0.058 0.06

Ciclosporin Posterior reversible encephalopathy syndrome

127 98 29 0.126 -0.08

Gabapentin Blood glucose decreased and hypoglycaemia

76 58 18 0.026 0.39

Aripiprazole Hypothyroidism 28 14 14 0.016 0.68

Natalizumab Cervical dysplasia 17 14 3 0.591 n.a.

Medroxy pro ges-ter one

Injection site necrosis and injection site atrophy

30 28 2 1.00 n.a.

Proguanil hydrochloride/ Atovaquone

Psychotic disorder 11 9 2 0.036 n.a.

Aripiprazole Psychosis aggravated 13 12 1 0.667 n.a.

Clindamycin Acute generalised exanthematous pustulosis

8 7 1 0.250 n.a.

Ceftriaxone Hepatitis 15 14 1 0.400 n.a.

Clarithromycin Angioedema 26 25 1 0.077 n.a.

Hydroquinine Hypoglycaemia 2 1 1 1.00 n.a.

Iobitridol Ventricular fibrillation 1 1 0 n.a. n.a.

Adalimumab Neuroendocrine carcinoma of the skin

5 5 0 n.a. n.a.

Nitrofurantoin Cutaneous vasculitis 1 1 0 n.a. n.a.

Tocilizumab Necrotising fasciitis 6 6 0 n.a. n.a.

Omeprazole Subacute cutaneous lupus erythematosus

4 4 0 n.a. n.a.

Fumaric acid Progressive multifocal leukoencephalopathy

2 2 0 n.a. n.a.

Lamotrigine Alopecia 453 88 365 0.912 n.a.

Paroxetine migraine 176 35 141 0.002 -0.10 Tamsulosin Vision blurred, visual acuity

reduced and visual impairment

151 39 112 0.250 0.05 escitalopram headache 235 128 107 0.140 0.06 fluticasone Palpitations 118 19 99 0.568 -0.01 Quetiapine Paraesthesia 165 84 81 <0.001 0.20 Lamotrigine nightmare 77 12 65 0.099 -0.16 Levonorgestrel Galactorrhoea 75 23 52 0.228 0.07

Quetiapine Sleep apnoea syndrome 69 31 38 0.062 0.10 Omeprazole Faeces discoloured 54 17 37 <0.001 0.35 Isotretinoin erectile dysfunction 59 28 31 0.331 0.12 Tamsulosin Dry mouth 49 21 28 0.437 -0.05

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Drug aDR Total

number of reports number of healthcare professional reports number of patient reports mann-whitney u test, p-value Ratio†

Rivastigmine nightmare and abnormal dreams 33 13 20 0.137 0.20 Tamsulosin Depression and depressed mood 30 12 18 0.368 0.08

Doxycycline Paraesthesia 49 32 17 0.179 -0,15

Sitagliptin Dyspnoea 135 121 14 <0.001 0,25

Dutasteride Testicular pain 20 6 14 0.659 -0,11 Metronidazole Oedema peripheral 35 24 11 0.958 0,00 Doxycycline Skin discolouration, skin

hyperpigmentation and pigmentation disorder

18 8 10 0.122 0,09

Terbinafine Anosmia. parosmia. hyposmia 43 36 7 0.392 -0,14 Trazodone Urinary incontinence 24 18 6 1.00 -0,56

Isotretinoin Anal fissure 15 9 6 0.864 -0,12

Omeprazole Erectile dysfunction 14 9 5 0.518 0,01

Azathioprine Chromaturia 12 8 4 0.683 0,16

Metronidazole Tongue discolouration 8 4 4 1.00 0.06 Azathioprine Photosensitivity reaction 13 9 4 0.825 0,04

Tramadol Anorgasmia 6 2 4 0.267 n.a.

Fluconazole Drug eruption 31 28 3 0.875 0.04

Tramadol Hiccups 12 9 3 0.282 0.04

Methylphenidate Epistaxis 19 17 2 0.140 n.a.

Pandemrix Injection site discolouration 4 3 1 1.00 n.a. Duloxetine Electric shock sensation 6 5 1 0.667 n.a.

Lenalidomide Psoriasis 4 3 1 1.00 n.a.

Mirtazapine Urinary retention 27 26 1 0.296 n.a.

Nadroparin Headache 10 9 1 0.200 n.a.

Terbinafine Hypoacusis 1 1 0 n.a. n.a.

Desloratadine Increased appetite 3 3 0 n.a. n.a.

Mercaptopurine Photosensitivity reaction 2 2 0 n.a. n.a. Buprenorphine Skin depigmentation 2 2 0 n.a. n.a.

Prednisolone Hiccups 4 4 0 n.a. n.a.

Betahistine Hallucination 2 2 0 n.a. n.a.

Terbinafine Erectile dysfunction 3 3 0 n.a. n.a.

Signals are sorted from IME signals to non-IME signals. And within the IME and non-IME signals they are sorted from highest number of patient reports to lowest

† Ratio calculated by: the difference in median days between reports by patients and healthcare profes-sionals divided by the number of days until signal detection

Signals in italic: classified as Important Medical Events (IMEs) Signals in bold: first ADR report was made by a patient

In case of p<0.05 the group of reporters that reported earlier is made bold n.a.is not applicable

of 34.0%). The first report was made by a patient for 4 IMEs (22.2%) and 8 non-IMEs (19.0%).

Patient reports were from 24 different countries: Belgium, Bulgaria, Canada, the Democratic Republic of the Congo, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Hungary, Iceland, Morocco, the Netherlands, Norway, Peru, Portugal, Slovakia, Sweden, Switzerland, Turkey, United Kingdom, and the USA. A total of 2124 reports came from the USA (61.9% patient reports) and 430 from Europe (21.9% patient reports) and 268 from non-European countries. For reports from the USA, 26.8% of the healthcare professional reports were classified as IMEs and 7.2% of the patient reports. For reports from Europe, 25.4% of the healthcare professional reports were classified as IMEs, and 37.2% of the patient reports.

Comparison in time to reporting

The overall cumulative distribution of time to reporting of patients and healthcare professionals is shown in Figure 2. The corresponding Mann-Whitney U test sug-gested that there was a statistically significant difference between these distributions (p<0.001). Healthcare professionals generally reported earlier than patients with a median time to reporting of 7.0 vs 8.3 years, and corresponding interquartile ranges of respectively 3.9 – 9.5 and 6.2 – 10.4 years. For IMEs, healthcare professionals and patients took a median time to reporting of 6.9 vs 8.1 years and for non-IMEs 7.0 vs 8.2 years (Figure 3a-b). In both cases, there was an overall statistically significant difference in the time distribution (p < 0.001). The cumulative distributions of reports from the USA and Europe are shown in Figure 4a-b. For the USA, median time to reporting for healthcare professionals and patients was 6.0 vs 8.1 years and for Eu-rope 7.8 vs 7.9 years. The corresponding tests for distribution differences were both significant, p<0.001 and p=0.03, respectively. In addition, healthcare professionals

Figure 2. The cumulative distribution of time of ADR reports, after the first ADR report, coming from

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in the USA reported earlier compared to those in Europe (p<0.001). For patients, no statistically significant difference was shown (p=0.531).

Individual signals

The analysis of the individual signals showed that for seven signals a statistically significant difference in time to reporting between the two groups was present (Table 1). For two of these signals, patients reported significantly earlier than healthcare professionals: ‘paroxetine associated with migraine’ (p=0.002) and ‘proguanil hydro-chloride/atovaquone associated with psychotic disorder’ (p=0.036).

To explore the meaning of the differences in time to reporting between patients and healthcare professionals, the difference in median days between reports by patients

Figure 3a-b. The cumulative distribution of time of ADR reports, after the first ADR report, coming from

patients and healthcare for:

a) IMEs, Mann-Whitney U p-value <0.001 b) non-IMEs, Mann-Whitney U p-value of <0.001

Figure 4a-b. The cumulative distribution of time of ADR reports, after the first ADR reports, coming from

patients and healthcare for:

a) study cases coming from the USA, Mann-Whitney U p-value <0.001 b) study cases coming from Europe, Mann-Whitney U p-value of 0.03

and healthcare professionals divided by the number of days until signal detection, was plotted against the number of days until signal detection (see Figure 5). A positive ratio means earlier reporting by healthcare professionals and a negative ratio earlier reporting by patients. The ratio-lines in the figure give an indication of the meaning of the difference in median between both groups. A small ratio in combination with a high number of days until signal detection indicated little clinical relevance, while a high ratio in combination with a small number of days until signal detection indicated a higher level of clinical relevance. In total, 34 signals were included in the scatter plot, of those 5 were classified as IMEs and 29 as non-IMEs. 19 out of 34 signals had a ratio between -0.1 and 0.1; 3 of those signals were classified as IMEs and 16 as non-IMEs. For 1 signal there was no difference between patients and healthcare professionals, for 11 signals, patients reported earlier and for 22 healthcare profes-sionals reported earlier. For patients, there was 1 signal with a ratio of less than -0.3. For healthcare professionals, there were 3 signals with a ratio over 0.3, including 2 classified as IMEs.

Figure 5. Scatterplot of the difference in median days between reports by patients and healthcare

pro-fessionals divided by the number of days until signal detection, plotted against the number of days until signal detection

closed bullet = signal classified as non-IME; open bullet = signal classified as IME

The ratio was calculated by the difference in median divided to the number of days until signal detec-tion. A positive ratio means earlier reporting by healthcare professionals and a negative ratio earlier reporting by patients.

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With the upcoming interest in patients as stakeholders in pharmacovigilance, it is important to explore the impact of patient reporting on early detection of new drug safety signals in pharmacovigilance. We demonstrated that ADRs which led to drug safety signals were generally reported earlier by healthcare professionals than patients, with an overall median difference of 1.3 years. This difference was present for ADRs classified as IMEs as well as non-IMEs. Although for the USA a difference in timing between both groups was present, for Europe the difference was negligible. The ratios in time to reporting were small, indicating that the difference in time to reporting ADRs between patients and healthcare professionals had limited impact on the overall time to signal detection for most signals.

It has been suggested that patient reports might enable earlier signal detection [20;21]. In 1996, Egberts et al. compared information obtained from patients and healthcare professionals on the, at the time, new antidepressant paroxetine [21]. At that time in the Netherlands, patients were not yet able to report directly to the pharmacovigilance centre, but could consult a telephone medicines information service maintained by pharmacists. Comparing the timing of reports by healthcare professionals to the national pharmacovigilance centre with questions by patients to the telephone service, showed that patients posted questions to this telephone service earlier as compared to healthcare professionals, with a mean time lag for all suspected reactions of 229 days. Hammond et al. explored time to signal detection for four randomly selected GlaxoSmithKline (GSK) marketed drugs, for reports of patients and healthcare professionals combined and as separate groups [33]. Using disproportionality analysis, 23 signals of disproportionate reporting were identi-fied, of which 52.2% (12 of 23) at an earlier stage when the patient reports were included, 34.8% (8 of 23) in the same year and 13% (3 of 23) later when patient reports where included. The aforementioned studies focussed on time-aspects of statistical drug-ADR reporting associations not necessarily representing safety signals. To our knowledge, including actual drug safety signals to compare time to reporting between patients and healthcare professionals has not been explored before.

In order to find a new drug safety signal, a certain amount of reports is necessary. The introduction of direct patient reporting introduced a growth in the number of reports by patients. This growth also reflects in the number of patient reports that contributed to new drug safety signals [16]. In the current study, we found a relatively high proportion of patient reports in the included signals; 52.7% of all reports and a range of 0% to 84.4% for the individual signals. Reports by patients are more represented in ADRs classified as non-IMEs than IMEs; range of 0% – 84.4% versus

0% – 55.1% respectively. Analysing signals individually, we demonstrated that for some, patients were earlier in reporting, and for others healthcare professionals. It is for this reason plausible that reports by patients can contribute to earlier signal detection. There are some points to consider concerning the data used for this study. In our study, over 60% of the reports from the USA originated from patients. This was higher than in another analysis from the USA, which showed that from 2006 to 2014 an average of 47% of all reports were from patients [3]. This may be explained by the nature of the selected signals. It was furthermore striking that the percentage reports classified as IME was higher for patient reports from Europe compared to those coming from the USA. The percentage IMEs included in all patient reports was in line with previous results of a study on Dutch drug safety signals by van Hunsel

et al. They showed that of all reports by patients that contributed to a signal in the

Netherlands from 2010 to 2015, 30.5% included an ADR classified as IME. This was a higher percentage than reports by healthcare professionals (22.5%) [16].

By selecting reports from the international database VigiBase, we could include a high number of reports which allowed us analysing signals by importance of the event and by region of origin. It must be kept in mind that data pooling can influence the outcome. On average, the median time to signal detection, calculated from time zero, was 10.4 years. Given the large variation in number of reports per signal, signals with a lot of reports contributed to a larger extent to the overall outcome. To place our results in perspective, we therefore also explored all signals individually.

The reporting rate may vary over time and may differ between patients and healthcare professionals. It can be influenced by factors, such as media attention or discussions on the internet [34;35]. As far as we know, there was no specific media attention for the drug-ADR associations included in our study, but differences in tim-ing due to external factors cannot be ruled out. In addition, for Europe due to changes in the pharmacovigilance legislation in 2012, it is possible that this legal change caused a steeper growth in patient reporting compared to healthcare professional reporting. This may have contributed to the difference in time to reporting we found between healthcare professional reports from the USA versus Europe.

ConCLusIon

Patients contributed a large proportion of reports on drug-ADR pairs that eventually became drug safety signals; 53% overall, with a median of 25%. This corroborates earlier findings on the contribution of patient reports to signal detection in pharmaco-vigilance. For all signals, median time to signal detection was 10.4 years. Healthcare

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professionals generally reported 1.3 year earlier than patients. This was the case for ADRs classified as IMEs as well as non-IMEs. This highlights an opportunity to further increase the value of patient reporting in the future, by encouraging patients to report suspected ADRs earlier.

RefeRenCes

1. The EU Pharmacogivilance system [online]. http://ec europa eu/health/human-use/pharma-covigilance/index_en htm 2015 [accessed 16 A.D. Oct 12].

2. van Hunsel F, Härmark L, Pal S, Olsson S, van Grootheest K. Experiences with Adverse Drug Reaction Reporting by Patients; An 11-Country Survey. Drug Saf 35[1], 45-60. 2012.

3. Food and Drug Administration. FAERS Report-ing by Healthcare Providers and Consumers by Year. http://www fda gov/Drugs/GuidanceCom-plianceRegulatoryInformation/Surveillance/ AdverseDrugEffects/ucm070456 htm 2016 [accessed 2016 Dec 23].

4. The EU Pharmacogivilance system [online]. http://ec europa eu/health/human-use/phar-macovigilance/index_en htm 2012 [accessed 2017 Mar 28].

5. Harmark L, Raine J, Leufkens H, Edwards IR, Moretti U, Sarinic VM, et al. Patient-Reported Safety Information: A Renaissance of Phar-macovigilance? Drug Saf 2016 Oct; 39(10): 883-90.

6. Aagaard L, Nielsen LH, Hansen EH. Consumer reporting of adverse drug reactions: a retro-spective analysis of the Danish adverse drug reaction database from 2004 to 2006. Drug Saf 2009; 32: 1067-1074.

7. de Langen J, van Hunsel F, Passier A, de Jong- van den Berg LTW, van Grootheest AC. Adverse Drug Reaction Reporting by Patients in the Netherlands, Three Years of Experience. Drug Saf 2008; 31: 515-524.

8. Chebane L, Abadie D, Bagheri H, Durrieu G, Montastruc J. Patient reporting of adverse drug reactions; experience of Toulouse Regional Pharmacovigilance Center (abstract ISOP 2012). Drug Saf 2012; 35: 877-970.

9. Clothier H, Selvaraj G, Easton M, Lewis G, Crawford N, Buttery J. Consumer reporting of adverse events following immunization. Hum Vaccin Immunother 2014; 10: 3726-3730.

10. Durrieu G, Palmaro A, Pourcel L et al. First French experience of ADR reporting by patients after a mass immunization campaign with Influenza A (H1N1) pandemic vaccines: a comparison of reports submitted by patients and healthcare professionals. Drug Saf 2012; 35: 845-854.

11. McLernon DJ, Bond CM, Hannaford PC et al. Adverse drug reaction reporting in the UK: a retrospective observational comparison of yellow card reports submitted by patients and healthcare professionals. Drug Saf 2010; 33: 775-788.

12. van Grootheest A, Passier J, van Puijenbroek E. Direct reporting of side effects by the patient: favourable experience in the first year (article in Dutch). Ned Tijdschr Geneeskd 2005; 149: 529-533.

13. Leone R, Moretti U, D’Incau P et al. Effect of pharmacist involvement on patient reporting of adverse drug reactions: first Italian study. Drug Saf 2013; 36: 267-276.

14. Rolfes L, van Hunsel F, Wilkes S, van Groot-heest K, van Puijenbroek E. Adverse drug reaction reports of patients and healthcare professionals-differences in reported informa-tion. Pharmacoepidemiol Drug Saf 2014 Jul 31. 15. L, van Hunsel F, van der Linden L, Taxis K, van Puijenbroek E. The Quality of Clinical Information in Adverse Drug Reaction Reports by Patients and Healthcare Professionals: A Retrospective Comparative Analysis. Drug Saf 2017 Jul; 40(7): 607-14.

16. van Hunsel F, de Waal S, Harmark L. The contribution of direct patient reported ADRs to drug safety signals in the Netherlands from 2010 to 2015. Pharmacoepidemiol Drug Saf 2017 May 19.

17. Foy M, Gandhi S, Cumber S, Jadeja MM. The Yellow Card Scheme: Patient reporting of Adverse Drug Reactions and Signals they have Generated. Drug Saf 2011; 34(10): 903.

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Avery AJ. How do patients contribute to signal detection? : A retrospective analysis of sponta-neous reporting of adverse drug reactions in the UK’s Yellow Card Scheme. Drug Saf 2013 Mar; 36(3): 199-206.

19. Kheloufi F, Default A, Rouby F, Blin O, Micallef J. Comment on “Patient Reporting in the EU: Analysis of EudraVigilance Data”. Drug Saf 2017 Jul 4.

20. Mitchell AS, Henry DA, Sanson-Fisher R, O’Connell DL. Patients as a direct source of information on adverse drug reactions. BMJ 1988 Oct 8; 297(6653): 891-3.

21. Egberts TC, Smulders M, de Koning FH, Mey-boom RH, Leufkens HG. Can adverse drug reactions be detected earlier? A comparison of reports by patients and professionals. BMJ 1996 Aug 31; 313(7056): 530-1.

22. European Medicines Agency. Inclusion/ exclusion criteria for the “Important Medical Evens” list. http://www ema europa eu/docs/ en_GB/document_library/Other/2016/08/ WC500212100 pdf 2016 August 16 [accessed 2017 Feb 22].

23. European Medicines Agency. Important medical event term list (MedDRA version 19.1). www almp hr/fdsak3jnFsk1Kfa/ostale_stranice/ IME-List-19-1 xlsx 2016 September 2 [accessed 2016 Oct 15].

24. European Commission. Pharmacovigilance related activities of Member States and the European Medicines Agency concerning me-dicinal products for human use (2012-2014). http://ec europa eu/health//sites/health/files/ files/pharmacovigilance/pharmacovigilance-report-2012-2014_en pdf 2016 August 8 [ac-cessed 2017 Feb 8].

25. Banovac M, Candore G, Slattery J, Houyez F, Haerry D, Genov G, et al. Patient Reporting in the EU: Analysis of EudraVigilance Data. Drug Saf 2017 Jul; 40(7): 629-45.

26. Uppsala Monitoring Centre. http://www who-umc org/ 2016 [accessed 2016 Jan 1]. 27. Rolfes L., Kolfschoten J., van Hunsel F., van

Puijenbroek E. The validity and reliability of a

signal impact assessment tool. Pharmacoepide-miol Drug Saf 2016 Jul; 25(7): 815-9.

28. Watson S, Chandler RE, Taavola H, Harmark L, Grundmark B, Zekarias A, et al. Safety Concerns Reported by Patients Identified in a Collaborative Signal Detection Workshop using VigiBase: Results and Reflections from Lareb and Uppsala Monitoring Centre. Drug Saf 2017 Sep 20.

29. Netherlands Pharmacovigilance Centre Lareb. Quarterly report 2012-3, Overview on gener-ated pharmacovigilance signals and the actions of Lareb and the MEB. 2012.

30. Rolfes L, Kolfschoten J, van Hunsel F, Kooijman M, van Puijenbroek E. Actions in response to drug safety signals arising from a spontaneous reporting system: Retrospective study in The Netherlands. Int J Risk Saf Med 2016 Aug 22; 28(2): 115-23.

31. WHO Collaborating Centre for Durg Satistics Methodology - Norwegian Institute of Public Health. ATC - Structure and principles. https: //www whocc no/atc/structure_and_principles/ 2011 March 25 [accessed 2017 Mar 22]. 32. Medical Dictonary for Regulatory Activities.

http://www meddra org/ 2017 [accessed 2017 Mar 22].

33. Hammond IW, Rich DS, Gibbs TG. Effect of consumer reporting on signal detection: using disproportionality analysis. Expert Opin Drug Saf 2007 Nov; 6(6): 705-12.

34. van Hunsel F, Passier A, van Grootheest AC. Comparing patients’ and healthcare profes-sionals’ ADR reports after media attention. The

broadcast of a Dutch television programma about the benefits and risks of statins as an ex-ample. Br J Clin Pharmacol 2009; 67(5): 558-64.

35. Netherlands Pharmacovigilance Centre Lareb. Overview of reports of adverse drug reactions associated with changes of the package of Thy-rax® _{(levothyroxine) from a bottle to a blister.}

http://www lareb nl/Signalen/KWB_2014_4_ Thyrax_bottle_2 2015 November 1.